no code implementations • 29 Feb 2024 • Shruti P. Gadewar, Alyssa H. Zhu, Iyad Ba Gari, Sunanda Somu, Sophia I. Thomopoulos, Paul M. Thompson, Talia M. Nir, Neda Jahanshad
For one case-control study, we used a generative adversarial model for style-based harmonization to generate site-specific controls.
no code implementations • 18 Nov 2023 • Vladimir Belov, Tracy Erwin-Grabner, Ling-Li Zeng, Christopher R. K. Ching, Andre Aleman, Alyssa R. Amod, Zeynep Basgoze, Francesco Benedetti, Bianca Besteher, Katharina Brosch, Robin Bülow, Romain Colle, Colm G. Connolly, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Kathryn Cullen, Udo Dannlowski, Christopher G. Davey, Annemiek Dols, Jan Ernsting, Jennifer W. Evans, Lukas Fisch, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Hans J. Grabe, Nynke A. Groenewold, Dominik Grotegerd, Tim Hahn, J. Paul Hamilton, Laura K. M. Han, Ben J Harrison, Tiffany C. Ho, Neda Jahanshad, Alec J. Jamieson, Andriana Karuk, Tilo Kircher, Bonnie Klimes-Dougan, Sheri-Michelle Koopowitz, Thomas Lancaster, Ramona Leenings, Meng Li, David E. J. Linden, Frank P. MacMaster, David M. A. Mehler, Susanne Meinert, Elisa Melloni, Bryon A. Mueller, Benson Mwangi, Igor Nenadić, Amar Ojha, Yasumasa Okamoto, Mardien L. Oudega, Brenda W. J. H. Penninx, Sara Poletti, Edith Pomarol-Clotet, Maria J. Portella, Elena Pozzi, Joaquim Radua, Elena Rodríguez-Cano, Matthew D. Sacchet, Raymond Salvador, Anouk Schrantee, Kang Sim, Jair C. Soares, Aleix Solanes, Dan J. Stein, Frederike Stein, Aleks Stolicyn, Sophia I. Thomopoulos, Yara J. Toenders, Aslihan Uyar-Demir, Eduard Vieta, Yolanda Vives-Gilabert, Henry Völzke, Martin Walter, Heather C. Whalley, Sarah Whittle, Nils Winter, Katharina Wittfeld, Margaret J. Wright, Mon-Ju Wu, Tony T. Yang, Carlos Zarate, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya-Maldonado
Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter.
no code implementations • 13 Sep 2023 • Andre Altmann, Ana C Lawry Aguila, Neda Jahanshad, Paul M Thompson, Marco Lorenzi
The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes.
1 code implementation • 8 Sep 2023 • Eamonn Kennedy, Shashank Vadlamani, Hannah M Lindsey, Kelly S Peterson, Kristen Dams OConnor, Kenton Murray, Ronak Agarwal, Houshang H Amiri, Raeda K Andersen, Talin Babikian, David A Baron, Erin D Bigler, Karen Caeyenberghs, Lisa Delano-Wood, Seth G Disner, Ekaterina Dobryakova, Blessen C Eapen, Rachel M Edelstein, Carrie Esopenko, Helen M Genova, Elbert Geuze, Naomi J Goodrich-Hunsaker, Jordan Grafman, Asta K Haberg, Cooper B Hodges, Kristen R Hoskinson, Elizabeth S Hovenden, Andrei Irimia, Neda Jahanshad, Ruchira M Jha, Finian Keleher, Kimbra Kenney, Inga K Koerte, Spencer W Liebel, Abigail Livny, Marianne Lovstad, Sarah L Martindale, Jeffrey E Max, Andrew R Mayer, Timothy B Meier, Deleene S Menefee, Abdalla Z Mohamed, Stefania Mondello, Martin M Monti, Rajendra A Morey, Virginia Newcombe, Mary R Newsome, Alexander Olsen, Nicholas J Pastorek, Mary Jo Pugh, Adeel Razi, Jacob E Resch, Jared A Rowland, Kelly Russell, Nicholas P Ryan, Randall S Scheibel, Adam T Schmidt, Gershon Spitz, Jaclyn A Stephens, Assaf Tal, Leah D Talbert, Maria Carmela Tartaglia, Brian A Taylor, Sophia I Thomopoulos, Maya Troyanskaya, Eve M Valera, Harm Jan van der Horn, John D Van Horn, Ragini Verma, Benjamin SC Wade, Willian SC Walker, Ashley L Ware, J Kent Werner Jr, Keith Owen Yeates, Ross D Zafonte, Michael M Zeineh, Brandon Zielinski, Paul M Thompson, Frank G Hillary, David F Tate, Elisabeth A Wilde, Emily L Dennis
An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues.
no code implementations • 1 May 2023 • Shruti P. Gadewar, Elnaz Nourollahimoghadam, Ravi R. Bhatt, Abhinaav Ramesh, Shayan Javid, Iyad Ba Gari, Alyssa H. Zhu, Sophia Thomopoulos, Paul M. Thompson, Neda Jahanshad
A quality control algorithm is also built-in, trained on the midCC shape features.
no code implementations • 16 Jun 2022 • Vladimir Belov, Tracy Erwin-Grabner, Ali Saffet Gonul, Alyssa R. Amod, Amar Ojha, Andre Aleman, Annemiek Dols, Anouk Scharntee, Aslihan Uyar-Demir, Ben J Harrison, Benson M. Irungu, Bianca Besteher, Bonnie Klimes-Dougan, Brenda W. J. H. Penninx, Bryon A. Mueller, Carlos Zarate, Christopher G. Davey, Christopher R. K. Ching, Colm G. Connolly, Cynthia H. Y. Fu, Dan J. Stein, Danai Dima, David E. J. Linden, David M. A. Mehler, Edith Pomarol-Clotet, Elena Pozzi, Elisa Melloni, Francesco Benedetti, Frank P. MacMaster, Hans J. Grabe, Henry Völzke, Ian H. Gotlib, Jair C. Soares, Jennifer W. Evans, Kang Sim, Katharina Wittfeld, Kathryn Cullen, Liesbeth Reneman, Mardien L. Oudega, Margaret J. Wright, Maria J. Portella, Matthew D. Sacchet, Meng Li, Moji Aghajani, Mon-Ju Wu, Natalia Jaworska, Neda Jahanshad, Nic J. A. van der Wee, Nynke Groenewold, Paul J. Hamilton, Philipp Saemann, Robin Bülow, Sara Poletti, Sarah Whittle, Sophia I. Thomopoulos, Steven J. A. van, der Werff, Sheri-Michelle Koopowitz, Thomas Lancaster, Tiffany C. Ho, Tony T. Yang, Zeynep Basgoze, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya-Maldonado
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders.
no code implementations • 24 Apr 2022 • Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg
Sensitivity analyses provide principled ways to give bounds on causal estimates when confounding variables are hidden.
no code implementations • 18 Nov 2020 • Pradeep Lam, Alyssa H. Zhu, Iyad Ba Gari, Neda Jahanshad, Paul M. Thompson
Building on a 3D convolutional neural network, we added two attention modules at different layers of abstraction, so that features learned are spatially related to the global features for the task.
no code implementations • 14 Oct 2017 • Nikita Mokrov, Maxim Panov, Boris A. Gutman, Joshua I. Faskowitz, Neda Jahanshad, Paul M. Thompson
This paper considers the problem of brain disease classification based on connectome data.
1 code implementation • 19 Jun 2017 • Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz, Boris Gutman, Daniel Moyer, Julio Villalon, Neda Jahanshad, Paul Thompson
There is no consensus on how to construct structural brain networks from diffusion MRI.
no code implementations • 26 May 2017 • Dajiang Zhu, Brandalyn C. Riedel, Neda Jahanshad, Nynke A. Groenewold, Dan J. Stein, Ian H. Gotlib, Matthew D. Sacchet, Danai Dima, James H. Cole, Cynthia H. Y. Fu, Henrik Walter, Ilya M. Veer, Thomas Frodl, Lianne Schmaal, Dick J. Veltman, Paul M. Thompson
Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature.
no code implementations • 27 Apr 2017 • Qingyang Li, Dajiang Zhu, Jie Zhang, Derrek Paul Hibar, Neda Jahanshad, Yalin Wang, Jieping Ye, Paul M. Thompson, Jie Wang
Then we select the relevant group features by performing the group Lasso feature selection process in a sequence of parameters.
1 code implementation • 2 Mar 2017 • Daniel Moyer, Boris A. Gutman, Neda Jahanshad, Paul M. Thompson
One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i. e. parcellation.
no code implementations • 26 Jan 2017 • Dmitry Petrov, Boris Gutman, Alexander Ivanov, Joshua Faskowitz, Neda Jahanshad, Mikhail Belyaev, Paul Thompson
In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains.
no code implementations • 18 Nov 2016 • Daniel Moyer, Boris A. Gutman, Joshua Faskowitz, Neda Jahanshad, Paul M. Thompson
In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes.
no code implementations • 19 Aug 2016 • Qingyang Li, Tao Yang, Liang Zhan, Derrek Paul Hibar, Neda Jahanshad, Yalin Wang, Jieping Ye, Paul M. Thompson, Jie Wang
To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD.